A deep learning approach to predict Hamburg rutting curve

Abstract

The Hamburg wheel tracking test (HWTT) is a widely used testing procedure designed to accelerate and simulate the rutting phenomena in the laboratory. Rut depth, as one of the outputs of the HWTT, is dependent on a number of parameters related to mix design and testing conditions. This study introduces a new model for predicting the rutting depth of asphalt mixtures using a deep learning technique - the convolution neural network (CNN). A database containing 10,000 data points from a comprehensive collection of HWTT results was used to develop a CNN-based machine learning prediction model. The model has been formulated in terms of known influencing mixture variables such as asphalt binder high-temperature performance grade, mixture type, aggregate size, aggregate gradation, asphalt content, total asphalt binder recycling content, and testing parameters, including testing temperature and number of wheel passes. The model can be used as a tool to estimate the rut depth in asphalt mixtures when laboratory testing is not feasible or for cost-saving, and pre-design trials.

KEYWORDS:

• Hamburg wheel tracking
• asphalt concrete
• rutting
• deep learning
• convolution neural network

Acknowledgements and disclaimer

The research team would like to, first and foremost, thank the Missouri Department of Transportation (MoDOT) and Missouri Asphalt Pavement Association (MAPA) for their generous support of this research. The authors would also like to thank colleagues at the Illinois Tollway. Finally, the research team would like to thank James Meister, and Grant Nichols from Mizzou Asphalt Pavement and Innovation Laboratory (MAPIL) for their assistance in during the course of this study. The findings and conclusions of this study were arrived at by the research team, and do not necessarily reflect the views and opinions of any of the official entities involved in this study (Missouri Department of Transportation, Missouri Asphalt Pavement Association, Oklahoma Department of Transportation, Asphalt Plus, LLC.).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Missouri Department of Transportation: [grant number 201712].